![]() Here, we present a growing database currently comprising 131 known chaotic dynamical systems, each paired with corresponding precomputed multivariate and univariate time series. Chaotic systems thus pose a unique challenge to modern statistical learning techniques, while retaining quantifiable mathematical properties that make them controllable and interpretable as benchmarks. Abstract: The striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, chaotic systems can be repeatedly measured to produce arbitrarily-detailed information about the underlying attractor.
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